Geographic routing protocols are potential in Flying Ad-Hoc Network (FANET) due to their flexibility. Location information is the basis of geographic routing protocols, however, existing geographic routing protocols pay more attention to the forwarding strategy and ignore how to realize the location service. The existing location service can not adapt to the high dynamics of FANET. In addition to this, geographic routing protocols do not consider the location and mobility relationship between nodes when designing forwarding strategies, which leads to a decrease in FANET reliability. In this paper, we propose a geographic routing protocol for FANET named GRP-APLS, which consists of Adaptive Prediction-Based Location Service (APLS) and Relative Forwarding. APLS maintains nodes' information using an adaptive location update mechanism and kalman filter location prediction. Relative Forwarding considers the relative location and relative mobility of nodes to select the optimal next hop. Simulation and real-world experiments demonstrate that APLS improves location accuracy by 65 % while reducing overhead by 44%, and GRP-APLS improves PDR by 20 % while reducing delay by 75 %.

The all-reduce operation is critically important for communication-intensive workloads emerging at the convergence of High-Performance Computing (HPC) and Internet of Things (IoT) applications. However, existing optimization efforts primarily concentrate on offloading the all-reduce onto network switches, known as In-Network Aggregation, which are incompatible with switchless torus topologies. Driven by our systematic analysis, we identified two key factors that impact the performance of the standard ring all-reduce operation: i. The all-reduce computation process frequently interrupts the GPU/CPU’s computation tasks; ii. The GPU/CPU is, in fact, indifferent to intermediate computational results. Based on this insight, we propose DirectReduce, a fully offloading ring all-reduce architecture that is comprised of three components: (i) the GateKeeper module, responsible for evaluating outgoing data to decide its progression-either directing it to the Protocol Engine for packetization or intercepting it for reduction (e.g., sum, maximum); (ii) the DataDirector module, which classifies the incoming data either is for intermediate result reduction or final result storage; and (iii) the ComputeEnhancer module, designed to execute reduction operations directly on the SmartNIC. Extensive simulation results show that DirectReduce can reduce the ring all-reduce latency by up to 1.98X in a ring (1D-torus) topology, 1.97X in a 3D-torus topology, and 1.75X in a 6D-torus topology compared to the standard ring all-reduce.

Cellular operators face significant challenges in cutting operating expenses while maintaining the quality of service (QoS) for users due to growing network traffic and dynamic user connections. These challenges are addressed by the cloud radio access network (C-RAN) architecture, which includes a centralized pool of baseband units (BBUs) and distributes them from remote radio heads (RRHs). The key to improving C-RAN performance is to dynamically allocate large-scale RRHs to different BBUs in real time. In this paper, we propose a user behavior-aware RRH-BBU mapping framework to improve the performance of large-scale C-RANs by predicting RRH traffic and users in advance. First, we propose a Multivariate RRH time series Prediction Model (MRPM) that captures the spatio-temporal patterns in the data to predict the traffic volume and the number of users of RRHs, which represents key indicators of RRH connection states. Second, we formulate the RRH-BBU mapping as a Markov decision process problem to optimize cost and QoS by considering BBU utilization, BBU energy consumption, RRH migration frequency, and BBU load balancing. Third, we propose a prediction-based RRH-BBU mapping scheme (PB-RBM) to find the optimal RRH-BBU mapping strategy by leveraging the prediction information of MRPM. In the PB-RBM algorithm, we employ an A3C algorithm to learn the mapping policy and group the RRHs based on a defined popularity metric to reduce the state and action space of the reinforcement learning algorithm. Finally, extensive experiments are conducted on a real-world dataset, and our algorithm is compared with several matching algorithms, such as ACKTR, heuristic, etc., to demonstrate its superiority, especially reducing 17.5% in RMSE compared to the best-performing baseline.

The autonomous exploration task we consider requires Unmanned Aerial Vehicles (UAVs) to actively navigate through unknown environments with the goal of fully perceiving and mapping the environments. Some existing exploration strategies suffer from rough cost budgets, ambiguous Information Gain (IG), and unnecessary backtracking exploration caused by Fragmented Regions (FRs). In our work, a hierarchical spatio-temporal-aware exploration framework is proposed to alleviate these problems. At the local exploration level, the Asymmetrical Traveling Salesman Problem (ATSP) is solved by comprehensively considering exploration time, IG, and heading consistency to avoid blindly exploring. Specifically, the exploration time is reasonably budgeted by fast marching in an artificial potential field. Meanwhile, a transformer-based map occupancy predictor is designed to assist in IG calculation by imagining spatial clues out of the Field of View (FoV), facilitating the prescient exploration. We verify that our local exploration is effective in alleviating the unnecessary back-and-forth movements caused by FRs and the interference of potential obstacle occlusion on the IG calculation. At the global exploration level, the classical Next Best View Points (NBVP) are generalized to Next Best Sub-Regions (NBSR) to choose informative sub-regions for further forward-looking exploration based on a well-designed utility function. Safe flight paths and dynamically feasible trajectories are reasonably generated throughout the exploration process by fast marching and B-spline curve optimization. Comparative simulations and benchmark tests demonstrate that our proposed exploration strategy is quite competitive in terms of exploration path length, total exploration time, and exploration ratio.

In the autonomous driving era, high-definition (HD) maps are an essential building block to enable fine-grained environmental perception, precise localization, and path planning. However, with rich multidimensional information, the size of HD map data is huge and cannot be stored onboard, where the dynamic map data need to be distributed in real time via vehicular networks and how to design the distribution mechanism (i.e., determining the data source for requests) becomes crucial. For the end-to-end communication protocols (i.e., TCP/IP), the main limitation is the vehicle mobility and high dynamic of the network topology, which can degrade the transmission performance dramatically. Therefore, in this article, we propose a reinforcement learning-based data source selection scheme, named RLSS, for efficient HD map distribution in vehicular named data networking (NDN) scenarios, which aims at seeking the best data source (roadside infrastructures or nearby vehicles) in accordance with the map data requests. Specifically, in RLSS, we adopt a deep reinforcement learning-based architecture to learn a neural network as an agent to make the decision of data source selection, which can work online after offline training based on historical selection action performance. In addition, to solve the “cold start” problem for a new vehicle, we propose a model aggregation algorithm and weight update approach to learn the model parameters from its nearby vehicles, which can guarantee the performance while saving the communication cost. Finally, we implement RLSS in NS-3 by adopting the tools of the ndnSIM, SUMO, and Gym. Extensive simulations demonstrate that RLSS can significantly improve the transmission performance in terms of delay, throughput, and packet loss when compared with state-of-the-art data source selection schemes.

Different from cloud computing, edge computing moves computing away from the centralized data center and closer to the end-user. Therefore, with the large-scale deployment of edge services, it becomes a new challenge of how to dynamically select the appropriate edge server for computing requesters based on the edge server and network status. In the TCP/IP architecture, edge computing applications rely on centralized proxy servers to select an appropriate edge server, which leads to additional network overhead and increases service response latency. Due to its powerful forwarding plane, Information-Centric Networking (ICN) has the potential to provide more efficient networking support for edge computing than TCP/IP. However, traditional ICN only addresses named data and cannot well support the handle of dynamic content. In this article, we propose an edge computing service architecture based on ICN, which contains the edge computing service session model, service request forwarding strategies, and service dynamic deployment mechanism. The proposed service session model can not only keep the overhead low but also push the results to the computing requester immediately once the computing is completed. However, the service request forwarding strategies can forward computing requests to an appropriate edge server in a distributed manner. Compared with the TCP/IP-based proxy solution, our forwarding strategy can avoid unnecessary network transmissions, thereby reducing the service completion time. Moreover, the service dynamic deployment mechanism decides whether to deploy an edge service on an edge server based on service popularity, so that edge services can be dynamically deployed to hotspot, further reducing the service completion time.

The multi-path protocol improves end-to-end throughput and becomes one of the solutions to improve video streaming Quality of Experience (QoE). Scalable Video Coding (SVC) encodes a video segment into multi-layer, and different layers can be transmitted on heterogeneous paths. SVC has great potential to make use of the multipath protocol. Meanwhile, multipath QUIC (MPQUIC) provides multiplexing and instant handshake compared with multipath transmission control protocol (MPTCP). Thus MPQUIC has great potential in Dynamic Adaptive Streaming over HTTP (DASH) with SVC. However, mismatches between streams with different priorities and paths, network congestion caused by the suddenly increased data traffic are new challenges of MPQUIC for DASH-SVC. Adaptive Stream-scheduler Multipath QUIC (ASMQ) framework is proposed to improve the user’s QoE in DASH-SVC, prioritizing the streams based on the DASH-SVC application information and evaluates the qualities of multipath. ASMQ schedule prioritized streams to the path with different qualities. In addition, the server-client feedback mechanism of ASMQ can adapt to network congestion conditions in real-time. Emulation with real network traces demonstrates that the ASMQ improves user’s QoE compared to MPTCP and traditional MPQUIC.

Named Data Networking (NDN) is considered as a prominent architecture towards future Wireless Local Area Networks (WLAN), and multicast plays an important role in data delivery such as media streaming, multipoint videoconferencing, etc. However, to achieve high-efficiency multicast in NDN WLAN is challenging for two significant reasons. First, without feedback mechanism in IEEE 802.11 standards, to guarantee reliability, the current multicast scheme transmits the multicast data with the basic rate (e.g., 1 Mbps for IEEE 802.11b), which inevitably increases the transmission delay for high-speed consumers. Second, as a NDN multicast group is constituted by consumers who are requesting the same content, multicast groups are easy to form and evolve rapidly, where a data rate adaptation scheme is requisite to accommodate differential multicast groups. In this paper, we propose a multi-stage multicast rate adaptation scheme for NDN WLAN, namedNDN-MMRA, to minimize the total transmission time with reliability guarantee for multicast group members. InNDN-MMRA, by checking the Pending Interest Table (PIT) status information, the number of consumers in each multicast group as well as their receiving capabilities are known ahead; with the available data rates in a specific 802.11 standard,NDN-MMRAdetermines: 1) how many transmission stages are required; and 2) in each stage, which data rate should be adopted. The merit is that with multi-stage transmissions, the data rate can be adapted in descending order to accommodate high-speed consumers with delay minimized, and low-speed consumers with reliability guaranteed. We implementNDN-MMRAin NS-3 by adopting the ndnSIM module, and conduct extensive experiments to demonstrate its efficacy under different IEEE 802.11 standards and various underlying WLAN topologies.

In Named Data Networking (NDN) architecture, data can be obtained from multiple content sources (i.e., producers or caching nodes) with multiple paths, making the traditional end-to-end (i.e., TCP/IP) congestion control scheme invalid. In addition, the NDN multi-path discovery and management are still an open issue as the dynamic network topology changes. In this article, we propose a multi-path congestion control mechanism, named MPCC, which includes two major components, i.e., multi-path discovery and multi-path congestion control. Particularly, for multi-path discovery, we first devise apath tagto uniquely mark each sub-path in the forwarding process, and then propose a tag-aware forwarding strategy to discover and manage sub-paths. For multi-path selection and congestion control, we first integrate the metrics of packet loss, bandwidth, round trip time, and path centrality, for path assessment, based on which, we then leverage the Upper Confidence Bound (UCB) algorithm to select sub-paths in order to maximize the network throughput. In addition, for selected sub-paths, we have devised a sub-path window adaptation algorithm to avoid multi-path congestions. At last, we implement MPCC in ndnSIM and conduct extensive experiments for performance evaluation. Our results demonstrate that MPCC can discover all sub-paths in real-time for the multi-path scenario, and can effectively avoid multi-path congestions with improving throughput and reducing transmission time.

The energy consumption for wireless interface is important for the power-constraint mobile and sensor devices. To improve energy efficiency in WLAN (such as Wi-Fi), power saving mode (PSM) is proposed, with an attempt to manage the time spent in idle listening (IL) state. The challenge is that the receiver has no knowledge about when the pending data will arrival under end-to-end communication protocols (TCP/IP); therefore each station has to spend more time in IL to wait for the pending data. To address this problem, we propose NDN-PSM, in which NDN communication architecture is leveraged to cut down unnecessary IL time. In particular, we introduce two new power states in NDN-PSM, i.e., light doze and deep doze. As stations can check pending interest table (PIT) information to predict data arrival precisely, they can switch to deep doze or light doze intelligently. The inherent receiver-driven patterns of NDN can make each station effectively go to deep doze state for power saving. We have implemented NDN-PSM in NS-3 through ndnSIM and the simulation results demonstrate that NDN-PSM can effectively reduce IL time as well as total power consumption and meanwhile retain low transmission delay. Specifically, compared to the PSM mechanism, NDN-PSM can reduce the average power consumption up to 56%.

Multicast rate adaptation in WLAN has become a hot topic from the research community. However, wireless IP multicast lacks in feedback mechanism from the receiver and also no retransmission mechanism from packet loss. To achieve multicast reliability, multicast data is always transmitted at the basic data rate (e.g., 1Mbps for 802.11b). Moreover, wireless NDN multicast is essentially different from traditional wireless IP multicast. NDN multicast composition is based on Pending Interest Table (PIT) state information, which significantly increases the dynamics of the multicast groups. In this paper, we propose a multicast rate adaptation scheme to dynamically select the multicast transmission rate for NDN multicast communication. We present a mapping mechanism between the PIT entry and the MAC address of the station. To reduce the multicast transmission time, we propose a multi-rate multicast transmission scheme for the dynamic NDN multicast groups. In addition, we use the NDN caching mechanism to improve the reliability and reduce the transmission delay when the multicast data loss. Simulation results show that the proposed multicast rate adaptation scheme can effectively reduce transmission time while achieving lower delay and packet loss rate.

In current wireless local area networks, power saving mode (PSM) attempt to reduce the power consumption of station. However, WiFi devices typically spend more than 80% of time in idle listening (IL) state even when the PSM is deployed. To solve the problem, we propose NDN-PSM, which leverages NDN communication patterns to reduce the energy consumption. In NDN-PSM, station checks pending interest table (PIT) information to predict the pending data packet so that it can determine whether switch to doze state. The evaluation result shows that our proposal can effectively reduce IL time as well as total power consumption. Compared to PSM mechanism, NDN-PSM can reduce the power consumption up to 56.2%.